Data scientists

Resources for data scientists who want to boost their machine learning models with external data.

  • How to Improve Your Training Data for Vastly Better Machine Learning

    How to Improve Your Training Data for Vastly Better Machine Learning

    Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.

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  • 4 Steps You Must Take to Prepare for Predictive Model Deployment

    4 Steps You Must Take to Prepare for Predictive Model Deployment

    In this article, we explain how to get ready for predictive model deployment, from preparing data pipelines to retraining ML models.

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  • What Is Augmented Data Discovery with Explorium?

    What Is Augmented Data Discovery with Explorium?

    With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be The post What Is Augmented Data Discovery with...

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  • Top Tips for Data Preparation Using Python

    Top Tips for Data Preparation Using Python

    Your machine learning model is only as good as the data you feed into it. That makes data preparation (or cleaning, wrangling, cleansing, pre-processing, or any The post Top Tips for Data...

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  • How to Deploy and Future-Proof Your Models: From Theory to Production

    How to Deploy and Future-Proof Your Models: From Theory to Production

    It’s no secret that while most organizations understand the importance of machine learning, most initiatives never make it off the ground. Follow this guide to guarantee you make it to production.

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  • Domain Knowledge in Data Science: Are Your Models Ready for Business?

    Domain Knowledge in Data Science: Are Your Models Ready for Business?

    In this in-depth article, we explain how the right questions will help you get the domain knowledge you need for data science for business.

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  • How Will Data Privacy Look in The Future?

    How Will Data Privacy Look in The Future?

    Data privacy continues to be a major hurdle for risk officers. In this article, we explain how the global increase in data surveillance creates short term opportunities but long term risks.

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  • The Three Skills You Need to Instill in Your Data Science Team

    The Three Skills You Need to Instill in Your Data Science Team

    To succeed in the data science field, you need more than just technical acumen. We reveal the secrets to making your team indispensable.

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  • Why You Need Data Catalogs, Not Databases

    Why You Need Data Catalogs, Not Databases

    When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.

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  • AI is Making BI Obsolete, and Machine Learning is Leading the Way

    AI is Making BI Obsolete, and Machine Learning is Leading the Way

    Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.

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  • How to Navigate the New Data Science and Machine Learning Landscape

    How to Navigate the New Data Science and Machine Learning Landscape

    In this article, we explain how to turn the biggest data science challenges of the moment into business opportunities.

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  • Why Data Marketplaces Are the Future of the Data Economy

    Why Data Marketplaces Are the Future of the Data Economy

    Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

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  • How to Connect to the Data Ecosystem

    How to Connect to the Data Ecosystem

    In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

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  • How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.

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  • Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation19:15

    Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation

    Dedy Kredo, Head of Customer Facing Data Science at Explorium, presents about augmented data and feature discovery at Data Science Salon Austin.

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  • Data Bias and What it Means for Your Machine Learning Models

    Data Bias and What it Means for Your Machine Learning Models

    Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.

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  • Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Top tips to engage stakeholders at every stage of the data science project life cycle.

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  • Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –

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  • COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.

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  • Explorium: Product Overview1:03

    Explorium: Product Overview

    Explorium is the future of data science. We enable you to automatically connect to thousands of data sources, distill the best features, and deploy the best models.

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